Fast Face Image Synthesis with Minimal Training

We propose an algorithm to generate realistic face im-ages of both real and synthetic identities (people who donot exist) with different facial yaw, shape and resolution.The synthesized images can be used to augment datasets totrain CNNs or as massive distractor sets for biometric ver-ification experiments without any privacy concerns. Addi-tionally, law enforcement can make use of this technique totrain forensic experts to recognize faces. Our method sam-ples face components from a pool of multiple face images ofreal identities to generate the synthetic texture. Then, a real3D head model compatible to the generated texture is usedto render it under different facial yaw transformations. Weperform multiple quantitative experiments to assess the ef-fectiveness of our synthesis procedure in CNN training andits potential use to generate distractor face images. Addi-tionally, we compare our method with popular GAN modelsin terms of visual quality and execution time.